Remote Sensing Image Fusion using PCNN Model Parameter Estimation by Gamma Distribution in Shearlet Domain

Abstract Here the proposed approach deals with some adaptive parameters in pulse coupled neural network (PCNN) model which are highly suitable in image fusion. Initially, the source images are separately decomposed into multi-scaled and multi-directional bands by shearlet transform (ST). Later, the PCNN model is mapped between the decomposed low pass ST sub-bands which depends on linking pulse response and coupling strength with regional statistics of ST coefficients. The process of different high pass ST sub-bands and utilization of singular value decomposition (SDV) have been discussed in details. Finally, we have obtained fusion results by the inverse shearlet transformation (IST). The experimental results on satellite images show that the proposed method has good performance and able to preserve spectral information and high spatial details simultaneously like the original source images. The objective evaluation criteria and visual effect illustrate that our proposed method has a better edge over the prevalent image fusion methods.